\begin{abstract}
Exploiting {\it mobile} cameras embedded on the widely-used
smartphones to serve object tracking offers a new dimension to
reduce the deployment cost of the stationary cameras
and shorten the tracking latency,
but brings the challenges in efficient task assignment
and cooperations among workers due to
the requirement of Mobile Crowdsensing (MCS) system.
Most existing effort in the literature focuses on
object tracking with MCS where the workers
capture the moving object photos
at pre-calculated sites.
However, the contradiction between the tracking coverage and the system cost
in these MCS-based tracking solutions is sharpened
when tracking scenarios and worker number vary.
In this paper, we investigate the tracking region to
conduct the task assignment among  {\it top-k} most probable sensing locations,
which can achieve maximal tracking utility.
Specifically, we construct a $N$-Gram prediction model to
determine the $k$ tracking locations and
formulate the task assignment problem
solved by the Kuhn-Munkras algorithm,
respectively, laying a theoretical foundation.
The prediction model soundness is verified statistically
and the task assignment effectiveness is evaluated
via large scale real-world data simulations.

\keywords{Mobile Crowdsensing, object tracking, trajectory prediction, task assignment}
%Extensive simulations with real-world vehicle datasets
%are conducted to show the effectiveness of the proposed approach.
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%The model soundness is verified statistically and the analysis accuracy is evaluated via Monte Carlo simulation. We also use three examples to show how such analysis can guide the practical implementation of on-demand MDC.
%
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%Specifically, we construct queuing models to describe the First-Come-First-Serve-based MDC with a single and multiple MEs and solve them exactly and by approximation, respectively, laying a theoretical foundation.
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%Although exploiting cameras to track the trace of moving object offers a new idea to reduce trackers' time consumption, it results in a low analytical efficiency due to the problem of reasonable camera deployment and real-time tracker acknowledgement. Most existing studies focus on stationary camera systems where the target clues are obtained from a pre-deployed video surveillance system.
%However, the degree of coverage for these solutions will degrade along with the sparsity deployment of the camera network, while the cost of deployment and maintenance arises as cameras are densely deployed.
%To tackle these challenges and track object in real time, we investigate the ``mobile cameras'', i.e., engaging mobile crowdsensing (MCS) users to take photographs for these target objects.
%We propose a two-stage approach: 1) in the offline stage, the cluster representation-based N-Gram prediction model is utilized to limit the tracking region; 2) in the online stage, we formulate the online task assignment problem in vehicle tracking as a maximum weighted bipartite matching problem, which can be efficiently solved by the Kuhn-Munkras algorithm. Extensive simulations with real-world vehicle datasets are conducted to show the effectiveness of the proposed approach.
\end{abstract}
